from aitlas.datasets import (
    AIDDataset, 
    EurosatDataset, 
    PatternNetDataset, 
    Resisc45Dataset, 
    RSD46WHUDataset, 
    RSICB256Dataset,
    RSSCN7Dataset, 
    SAT6, 
    SiriWhuDataset, 
    UcMercedDataset, 
    WHURS19Dataset, 
    CLRSDataset
)

AID dataset

train_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/AID/train.csv"
}
train_dataset = AIDDataset(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 10000

	Label	Count
0	Airport	360
1	BareLand	310
2	BaseballField	220
3	Beach	400
4	Bridge	360
5	Center	260
6	Church	240
7	Commercial	350
8	DenseResidential	410
9	Desert	300
10	Farmland	370
11	Forest	250
12	Industrial	390
13	Meadow	280
14	MediumResidential	290
15	Mountain	340
16	Park	350
17	Parking	390
18	Playground	370
19	Pond	420
20	Port	380
21	RailwayStation	260
22	Resort	290
23	River	410
24	School	300
25	SparseResidential	300
26	Square	330
27	Stadium	290
28	StorageTanks	360
29	Viaduct	420

Eurosat dataset

train_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/EuroSAT/train.csv"
}
train_dataset = EurosatDataset(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 27000

	Label	Count
0	AnnualCrop	3000
1	Forest	3000
2	HerbaceousVegetation	3000
3	Highway	2500
4	Industrial	2500
5	Pasture	2000
6	PermanentCrop	2500
7	Residential	3000
8	River	2500
9	SeaLake	3000

PatternNet dataset

train_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/PatternNet/train.csv"
}
train_dataset = PatternNetDataset(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 30400

	Label	Count
0	airplane	800
1	baseball_field	800
2	basketball_court	800
3	beach	800
4	bridge	800
5	cemetery	800
6	chaparral	800
7	christmas_tree_farm	800
8	closed_road	800
9	coastal_mansion	800
10	crosswalk	800
11	dense_residential	800
12	ferry_terminal	800
13	football_field	800
14	forest	800
15	freeway	800
16	golf_course	800
17	harbor	800
18	intersection	800
19	mobile_home_park	800
20	nursing_home	800
21	oil_gas_field	800
22	oil_well	800
23	overpass	800
24	parking_lot	800
25	parking_space	800
26	railway	800
27	river	800
28	runway	800
29	runway_marking	800
30	shipping_yard	800
31	solar_panel	800
32	sparse_residential	800
33	storage_tank	800
34	swimming_pool	800
35	tennis_court	800
36	transformer_station	800
37	wastewater_treatment_plant	800

Resisc45 dataset

train_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/RESISC45/train.csv"
}
train_dataset = Resisc45Dataset(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 31500

	Label	Count
0	airplane	700
1	airport	700
2	baseball_diamond	700
3	basketball_court	700
4	beach	700
5	bridge	700
6	chaparral	700
7	church	700
8	circular_farmland	700
9	cloud	700
10	commercial_area	700
11	dense_residential	700
12	desert	700
13	forest	700
14	freeway	700
15	golf_course	700
16	ground_track_field	700
17	harbor	700
18	industrial_area	700
19	intersection	700
20	island	700
21	lake	700
22	meadow	700
23	medium_residential	700
24	mobile_home_park	700
25	mountain	700
26	overpass	700
27	palace	700
28	parking_lot	700
29	railway	700
30	railway_station	700
31	rectangular_farmland	700
32	river	700
33	roundabout	700
34	runway	700
35	sea_ice	700
36	ship	700
37	snowberg	700
38	sparse_residential	700
39	stadium	700
40	storage_tank	700
41	tennis_court	700
42	terrace	700
43	thermal_power_station	700
44	wetland	700

RSD46WHU dataset train split

train_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/RSD46-WHU/train/train.csv"
}
train_dataset = RSD46WHUDataset(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 99381

	Label	Count
0	Airplane	2575
1	Airport	1405
2	Artificial dense forest land	2388
3	Artificial sparse forest land	2404
4	Bare land	851
5	Basketball court	2534
6	Blue structured factory building	2611
7	Building	2940
8	Construction site	2786
9	Cross river bridge	1910
10	Crossroads	1741
11	Dense tall building	2608
12	Dock	2675
13	Fish pond	1372
14	Footbridge	2230
15	Graff	2559
16	Grassland	2408
17	Low scattered building	2039
18	Lrregular farmland	2665
19	Medium density scattered building	894
20	Medium density structured building	3015
21	Natural dense forest land	2550
22	Natural sparse forest land	2519
23	Oiltank	1368
24	Overpass	2128
25	Parking lot	2598
26	Plasticgreenhouse	863
27	Playground	2728
28	Railway	2646
29	Red structured factory building	2536
30	Refinery	2259
31	Regular farmland	2728
32	Scattered blue roof factory building	2593
33	Scattered red roof factory building	2497
34	Sewage plant-type-one	458
35	Sewage plant-type-two	364
36	Ship	2563
37	Solar power station	2579
38	Sparse residential area	2534
39	Square	2813
40	Steelsmelter	2494
41	Storage land	1798
42	Tennis court	1322
43	Thermal power plant	1074
44	Vegetable plot	2452
45	Water	2307

RSD46WHU dataset test split

test_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/RSD46-WHU/val/train.csv"
}
test_dataset = RSD46WHUDataset(test_dataset_config)

print(f"Total number of images: {len(test_dataset)}")
fig = test_dataset.show_batch(15)
fig = test_dataset.data_distribution_barchart()
test_dataset.data_distribution_table()

Total number of images: 17512

	Label	Count
0	Airplane	454
1	Airport	247
2	Artificial dense forest land	420
3	Artificial sparse forest land	424
4	Bare land	150
5	Basketball court	447
6	Blue structured factory building	460
7	Building	518
8	Construction site	491
9	Cross river bridge	337
10	Crossroads	307
11	Dense tall building	460
12	Dock	472
13	Fish pond	241
14	Footbridge	393
15	Graff	451
16	Grassland	424
17	Low scattered building	359
18	Lrregular farmland	470
19	Medium density scattered building	157
20	Medium density structured building	532
21	Natural dense forest land	450
22	Natural sparse forest land	445
23	Oiltank	241
24	Overpass	375
25	Parking lot	458
26	Plasticgreenhouse	152
27	Playground	481
28	Railway	466
29	Red structured factory building	447
30	Refinery	399
31	Regular farmland	481
32	Scattered blue roof factory building	457
33	Scattered red roof factory building	440
34	Sewage plant-type-one	80
35	Sewage plant-type-two	64
36	Ship	451
37	Solar power station	454
38	Sparse residential area	447
39	Square	496
40	Steelsmelter	439
41	Storage land	316
42	Tennis court	232
43	Thermal power plant	189
44	Vegetable plot	432
45	Water	406

RSICB256 dataset

train_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/RSI-CB256/train.csv"
}
train_dataset = RSICB256Dataset(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 24747

	Label	Count
0	airplane	351
1	airport_runway	678
2	artificial_grassland	283
3	avenue	544
4	bare_land	864
5	bridge	469
6	city_building	1014
7	coastline	459
8	container	660
9	crossroads	553
10	dam	324
11	desert	1092
12	dry_farm	1309
13	forest	1082
14	green_farmland	644
15	highway	223
16	hirst	628
17	lakeshore	438
18	mangrove	1049
19	marina	366
20	mountain	812
21	parkinglot	467
22	pipeline	198
23	residents	810
24	river	539
25	river_protection_forest	524
26	sandbeach	536
27	sapling	879
28	sea	1028
29	shrubwood	1331
30	snow_mountain	1153
31	sparse_forest	1110
32	storage_room	1307
33	stream	688
34	town	335

RSSCN7 dataset

train_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/RSSCN7/train.csv"
}
train_dataset = RSSCN7Dataset(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 2800

	Label	Count
0	farm_land	400
1	forest	400
2	grass_land	400
3	industrial_region	400
4	parking_lot	400
5	residential_region	400
6	river_lake	400

SAT6 train split

train_dataset_config = {
    "mat_file_path": "/media/hdd/multi-class/SAT/sat-6-full.mat",
    "mode": "train"

}
train_dataset = SAT6(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 324000

	Label	Count
0	barren land	14923
1	buildings	8192
2	grassland	56809
3	roads	50347
4	trees	73397
5	water bodies	120332

SAT6 test split

test_dataset_config = {
    "mat_file_path": "/media/hdd/multi-class/SAT/sat-6-full.mat",
    "mode": "test"

}
test_dataset = SAT6(test_dataset_config)

print(f"Total number of images: {len(test_dataset)}")
fig = test_dataset.show_batch(15)
fig = test_dataset.data_distribution_barchart()
test_dataset.data_distribution_table()

Total number of images: 81000

	Label	Count
0	barren land	3714
1	buildings	2070
2	grassland	14185
3	roads	12596
4	trees	18367
5	water bodies	30068

SiriWhu dataset

train_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/SIRI-WHU/train.csv"
}
train_dataset = SiriWhuDataset(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 2400

	Label	Count
0	agriculture	200
1	commercial	200
2	harbor	200
3	idle_land	200
4	industrial	200
5	meadow	200
6	overpass	200
7	park	200
8	pond	200
9	residential	200
10	river	200
11	water	200

UcMerced dataset

train_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/UCMerced/train.csv"
}
train_dataset = UcMercedDataset(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 2100

	Label	Count
0	agricultural	100
1	airplane	100
2	baseballdiamond	100
3	beach	100
4	buildings	100
5	chaparral	100
6	denseresidential	100
7	forest	100
8	freeway	100
9	golfcourse	100
10	harbor	100
11	intersection	100
12	mediumresidential	100
13	mobilehomepark	100
14	overpass	100
15	parkinglot	100
16	river	100
17	runway	100
18	sparseresidential	100
19	storagetanks	100
20	tenniscourt	100

WHURS19 dataset

train_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/WHU-RS19/train.csv"
}
train_dataset = WHURS19Dataset(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 1005

	Label	Count
0	Airport	55
1	Beach	50
2	Bridge	52
3	Commercial	56
4	Desert	50
5	Farmland	50
6	Forest	53
7	Industrial	53
8	Meadow	61
9	Mountain	50
10	Park	50
11	Parking	50
12	Pond	54
13	Port	53
14	Residential	54
15	River	56
16	Viaduct	58
17	footballField	50
18	railwayStation	50

CLRS dataset

train_dataset_config = {
    "csv_file_path": "/media/hdd/multi-class/CLRS/train.csv"
}
train_dataset = CLRSDataset(train_dataset_config)

print(f"Total number of images: {len(train_dataset)}")
fig = train_dataset.show_batch(15)
fig = train_dataset.data_distribution_barchart()
train_dataset.data_distribution_table()

Total number of images: 15000

	Label	Count
0	airport	600
1	bare-land	600
2	beach	600
3	bridge	600
4	commercial	600
5	desert	600
6	farmland	600
7	forest	600
8	golf-course	600
9	highway	600
10	industrial	600
11	meadow	600
12	mountain	600
13	overpass	600
14	park	600
15	parking	600
16	playground	600
17	port	600
18	railway	600
19	railway-station	600
20	residential	600
21	river	600
22	runway	600
23	stadium	600
24	storage-tank	600